Method to view schedule interdependencies and provide proactive clinical process decision support in day view form

ABSTRACT

A system and method for use in managing and preparing for scheduled procedures that are characterized as being interdependent and variable. The disclosed method enables schedule risk management and provides a look-ahead capability along with process diagnostics to isolate specific assets and tasks that can be managed to reduce schedule risk. The method facilitates review of upcoming tasks by the process stakeholders for education as to where the schedule risks reside and in an emulation mode for review and improved scheduling going forward. Clinical workflow is integrated such that process stakeholders and assets are directed in such a way as to keep on, reduce delay risk or recover the schedule.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 16/206,447, filed on Nov. 30, 2018, entitled “METHOD TO VIEWSCHEDULE INTERDEPENDENCIES AND PROVIDE PROACTIVE CLINICAL PROCESSDECISION SUPPORT IN DAY VIEW FORM,”, which claims priority to U.S.patent application Ser. No. 12/040,646, filed on Feb. 29, 2008, entitled“METHOD TO VIEW SCHEDULE INTERDEPENDENCIES AND PROVIDE PROACTIVECLINICAL PROCESS DECISION SUPPORT IN DAY VIEW FORM,” which claims thebenefit of priority under 35 U.S.C. 120 as a continuation-in-part toU.S. application Ser. No. 11/559,045, entitled “SYSTEM AND METHOD FORPROTOCOL ADHERENCE”, filed Nov. 13, 2006, which claims the benefit ofpriority to Provisional Application U.S. Ser. No. 60/737,219, entitled“CLINICAL PROCESS DECISIONING”, filed Nov. 15, 2005, all of which arehereby incorporated herein by reference in their entireties.

BACKGROUND

The invention relates generally to business scheduling systems, and moreparticularly to scheduling systems in the clinical setting, such ashealthcare delivery institutions or hospitals.

Healthcare delivery institutions are business systems that can bedesigned and operated to achieve their stated missions robustly. As isthe case with other business systems such as those designed to provideservices and manufactured goods, there are benefits to reducingvariation such that the stake-holders within these business systems canfocus more fully on the value added core processes that achieve thestated mission and less on activity responding to needless variationssuch as delays, accelerations, backups, underutilized assets, unplannedovertime by staff and stock outs of material, equipment, people andspace that were preventable and/or scheduled for. In this way the systemcan achieve its mission more reliably and be robust to exogenous forcesoutside of the process control.

Currently clinical process decisions have historically relied on the artof understanding symptoms and diagnosing causality much in alignmentwith the practice of the medical diagnosis arts. In an ever-evolvingenvironment, judgment and experientially-developed mental models areutilized by the healthcare providers to utilize the informationcurrently at hand to make decisions. Presented with similar data, thedecision made from one maker to another typically exhibits a largevariation. Presented with partial information, which is the byproduct ofbeing organized in functional departments, specialties, roles and by thenature of having partial and/or current or dated informationavailability on hand—clinical process decisions vary widely andtypically are locally focused for lack of a systems view upstream anddownstream of the decision point.

Where information systems exist, they are simply informational innature, Examples include scheduled rooms, people, materials andequipment. Recent advances in locating devices such as those utilizingradio-frequency identification (RFID) technology to report a location ofa tagged asset are utilized, yet personnel are loath to be tracked bywearing a device. RFID devices are not pervasive, and the systems thatmonitor them are not fully integrated with the requisite adjacentsystems that gather contextual information. The current art is notpredictive, probabilistic nor necessarily systemic. For example, knowingthe location of an asset is desirable but knowing its anticipated needand interdependencies is required to make a decision to use a locatedasset actionable. The information required for such a decision comesfrom a plurality of adjacent health information systems and must have anability to play forward into the future.

In the current art, current procedure duration and room status isprovided without any proactive or forward-looking capability. In thecurrent art, schedules are produced before a day's activities commence.In the current art, process status is displayed along with trending and,often, alarm functionality should a process variable trip a thresholdset point.

There is therefore a need for an integrated system and method forscheduling clinical activities and procedures that incorporatevariation, staff and equipment preferences, interdependencies andinformation flow into the clinical delivery of healthcare that can “lookahead” and enable “what-if” capability for prospective decision support.

SUMMARY OF THE INVENTION

Certain embodiments provide systems and methods for using probabilitiesto schedule activities in a clinical environment and modify schedulesand clinical processes in healthcare delivery. The method includes 1)loading a daily schedule of tasks for a clinical environment; 2)selecting protocols to occur in conjunction with the tasks; 3) selectingdecision rules to resolve interdependencies with respect to the tasks;4) selecting estimated task durations; 5) monitoring a state of persons(personnel and patients), equipment, and locations involved in theschedule in the clinical environment as the day progresses; and 6)modifying the schedule based on a) the state of the persons, equipment,and locations, b) estimated task durations, and c) interdependenciesbetween the persons, equipment, and location.

Certain embodiments provide a system for use in planning proceduressubject to variation. The system includes a user interface moduleconfigured to allow a user to monitor activities and track a schedule oftasks for a given day. The user interface module is configured to permitthe user to visualize variation of schedule task times, to visualizescheduling opportunities and constraints, and to view schedule riskinformation with respect to the schedule of tasks for the day. Thesystem also includes a logic engine coupled to the user interfacemodule. The logic engine is configured to load the schedule of tasks fora given day, along with decision rules and protocols and to calculate aschedule risk based on a) task interdependencies between personnel,equipment, and location, b) estimated task durations, and c) a state ofpersonnel, patient, equipment, and locations. The logic engine usesprocess feedback from the schedule of tasks along with probabilistichistorical data in conjunction with the schedule risk to provide analert to the user regarding the schedule risk.

Certain embodiments provide a computer-readable medium having a set ofinstructions for execution on a computer. The set of instructionsincludes a user interface routine for displaying a schedule of tasks fora given day and allowing a user to monitor activities and track theschedule of tasks along with visualization of timing probabilities,schedule risks, and proposed schedule modifications involving times,locations, personnel, and equipment. The set of instructions alsoincludes a probabilistic logic routine for calculating, based on a setof decision rules and schedule protocols, a schedule risk based on a)task interdependencies between personnel, equipment, and location; b)estimated task durations, and c) a state of personnel, patients,equipment, and locations. The probabilistic logic routine uses processfeedback from the schedule of tasks along with probabilistic historicaldata in conjunction with the schedule risk to provide an alert to theuser regarding the schedule risk.

Certain embodiments modify a schedule based on a) the state ofpersonnel, patients, equipment, and locations, b) estimated taskdurations, and c) interdependencies between the personnel, patients,equipment, and locations.

Certain embodiments calculate a schedule risk using assumptions derivedfrom process feedback from a schedule of tasks and probabilistichistorical task duration data and at least one of a) a critical pathmethod and b) program evaluation and review techniques.

Certain embodiments provide at least one of the following scenarios to auser for use in modifying a schedule as a day progresses: a) providing ahistorical “what was” emulation of the schedule; b) providing a current“what is” visualization of the schedule; c) providing a future “whatcould/will be” simulation of the schedule; and d) providing a predictive“what if” scenario generation for the schedule, based on theinterdependencies, estimated task durations, and the state of personnel,patients, equipment, and locations.

DESCRIPTION OF THE DRAWINGS

These and other features, aspects and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like part throughout the drawings. Theembodiments shown in the drawings are presented for purposes ofillustration only. It should be understood, however, that the presentinvention is not limited to the arrangements and instrumentality shownin the attached drawings.

FIG. 1 is an exemplary illustration of the effects of variation onscheduling procedures.

FIG. 2 is a block diagram illustration of an embodiment of a durationestimator module to which embodiments of the present invention areapplicable.

FIG. 3 is a block diagram illustration of an embodiment of a blockallocation planner module to which embodiments of the present inventionare applicable.

FIG. 4 is an illustration of a user display for an exemplary blockallocation planner module.

FIG. 5 is an exemplary illustration of another user display applicableto embodiments of a user interface.

FIG. 6 is a block diagram illustration of a system for use in planningclinical procedures in accordance with embodiments of the presentinvention.

FIG. 7 illustrates the Day View system in its process context accordingto an embodiment of the present invention.

FIG. 8 illustrates an exemplary setup and use of the Day View system inaccordance with an embodiment of the present invention.

FIG. 9 depicts an analytical roadmap to build decision support using DayView in accordance with an embodiment of the present invention.

FIG. 10 illustrates exemplary assets capable of use in a Day Viewscheduling process according to an embodiment of the present invention.

FIG. 11 illustrates clinical process management and value according toan embodiment of the present invention,

FIG. 12 illustrates dynamic system context and interaction between avariety of simulation and forecast modalities in accordance with anembodiment of the present invention.

FIG. 13 depicts an example embodiment of an archetype structure formacro objectives according to an embodiment of the present invention.

FIG. 14 illustrates an example of day plan forwarding and replayaccording to an embodiment of the present invention.

DETAILED DESCRIPTION

Certain embodiments provide systems and methods for systemicallyorganizing tasks and assets of a process to more effectively achieveimmediate and longer-term macro objectives. In certain embodiments,scheduled tasks are organized using, for example, a critical path method(CPM) and the tasks therein are exposed to durations which areprobabilistic and are either within the endogenous variation control ofthe system or are exogenous factors to which the system must be robustto. Measures of duration, availability and reliability to calculate anenumeration of scenarios in the context of variation is used todetermine the probabilities of meeting a selected schedule (schedulerisk). The probabilistic measures of duration, availability andreliability are functions of path dependent consumption and utilizationdecisions that are made to determine the use of the assets of theprocess. Using a multi-modality simulation methodology, for example, aprocess transfer function of the probabilistic measures may be derived.It is these estimates of duration, both endogenous and exogenous, thatare described by simulation that create the task durations and logic forinterdependencies which in turn are used to calculate schedule risk inthe CPM method. Certain embodiments provide decision support to helpmake effective decisions in real time or substantially real time whileembedded in a highly variable and interdependent process. These decisionsupport embodiments can be automated or prescriptive to the processstake-holder.

The present invention provides a method to review what has recentlyhappened in a process, to view actual current process operations, and toview what is on the schedule looking forward into the near term future.Specific assets such as plant & equipment, people, physical location andinformation are exemplary entities being tracked.

The present invention reduces process variation and thereby enablesstaff to focus more on the clinical delivery of healthcare. This isachieved in four ways—(1) An overarching capability to reduce internal(endogenous) variation from interdependency variation that can beanticipated and subsequently managed by techniques such as in certainembodiments of the presently disclosed technology; (2) To incorporatevariation into the process planning and control as far forward into thetime line such that not only more accurate averages are used forscheduling, but also the probability ranges of time duration for variousactivities, availability, capability ranges for staff and equipment areincorporated and planned for such as in certain embodiments of thepresently disclosed technology; (3) An overarching capability to combineinformation flows as to the status of staff, patients, equipment andfacilities with the scheduled plan such that anticipatory alerts areprovided when schedule risk crosses a threshold as well as a diagnosisas to the cause of the likely or actual source of the deviation that issufficient and actionable enough for intervention by the staff toresolve or revise a plan for it such as is provided for in part bycertain embodiments of the presently disclosed technology; and, (4) Anoverarching capability to understand and incorporate the effects ofexternal (exogenous) variation resulting from unforecastable events suchas, for example, surges, medical reason procedure delay, equipmentfailure and staff sickness such as is provided for in certainembodiments of the presently disclosed technology.

In accordance with a first embodiment of the present invention, a systemfor use in planning procedures is provided comprising the followingcomponents: a duration estimator module configured to characterizeaverage duration times and variations from average duration times for agiven procedure or activity; a block allocation planner moduleconfigured to schedule procedures or activities in accordance withcharacterized times from the duration estimator module; and a userinterface module configured to permit a user to visualize variation, tovisualize scheduling opportunities and constraints and to viewinformation output for use in scheduling procedures and activities. Eachcomponent or module will be described in greater detail below. In theexemplary embodiments described below, the given schedule is related toa perioperative environment. However it is to be appreciated that themethods and system can be extended to other clinical or non-clinicalprocess systems.

The Duration Estimator, which may be thought of as a ‘better ruler tomeasure with’, measures variation so that procedure times are moreaccurate and so that schedule risk is reduced.

The Block Allocation Planner may be thought of as a “defragmenter” forreserving blocks of time. Much like a computer hard drive defragmenter,it re-sorts the time slots and rooms to be available for the booking ofsurgeries in such a way as to satisfy constraints and departmentalobjectives. It factors in preferences and availabilities and solves forthe best departmental allocation of space and time and through theallocation, help achieve department policy objectives such as case mix,outcomes, safety and to provide incentive for desired behaviors.

The user interface to the disclosed schedule decision support mayinclude at least one of modules configured to permit a user to visualizevariation, to visualize scheduling opportunities and constraints andprovide output for use in scheduling procedures and activities, orcombinations thereof.

In accordance with an exemplary embodiment, the scheduling module isconfigured to aid in initially scheduling activities and procedures andis configured to visualize scheduling opportunities and constraints andwill be referred to herein as “Day Planner”. Day Planner may be thoughtof as a manufacturing requisition planner, it enables the scheduling ofspecific activities and all interdependencies—with the added benefit ofsimulating forward many alternative plans and contingencies.

In accordance with an exemplary embodiment, the user interface to theschedule manager as the process is occurring is configured to indicateand display variation along with suggestions as to “do-what” and enable“what-if” and will be referred to herein as “Day View”. Day View may bethought of as a “radar” for the clinical process, it brings schedulewith other location and clinical information so that the staff can knowwhen schedule deviations are occurring, what the cause is, have a way tovisualize process interdependencies, have the ability to play out orsimulate alternative process decisions and ultimately get the processstakeholders constructively involved in proceeding forward in a mannerthat has their intellectual buy-in to the course.

A user interface module is configured to provide output for use indirecting procedures and activities and will be referred to herein as“Provider/Patient Kiosk”. Provider/Patient Kiosk may be thought of asworkflow, the kiosk is information gathering and output for the variousstakeholders in the clinical process. It gathers information that islater used to reduce variation and it interacts with the stakeholders inmediums that are natural extensions of their native environments. Theuser interface may include computer display and/or reports.

It is to be appreciated that the clinical process decision supportmethods may comprise one or more of the capabilities described above, aswell as any combination thereof.

Endogenous variation is reduced within clinical process from situationsarising from concurrent scheduled use of mutually exclusive resourcessuch as doctors, nurses, equipment and space regardless of the causebeing endogenously or exogenously perturbed. Methods according to thepresent invention move clinical process decisioning as far forward intothe care process as possible. Known interdependencies are then broughtto each decision point and factored in so that interdependencies arethen managed and visible immediately. For example, in the instance ofday planning/scheduling done in advance of the day for which activity isscheduled, some interdependencies must be known well before blockschedules are derived and patients are scheduled.

Embodiments of the present invention address the immediate, inevitabledeviations from plan with diagnoses capability, in near real time, forthe causality of endogenously generated variation and provides decisionsupport for prospective process recovery to either the originallyproscribed state or a new, superior one, given other operatingconstraints that render the original plan less advantageous. Exogenousvariation—that which happens to the delivery system, despite the careand safety margin in the process scheduling, such as for exampleunanticipated medical complications, a surge of additional procedure(s)or an unanticipatable staffing change or equipment outage—is addressedby the ability to see the impact of change at or before it is currentlypossible to see it and then to provide feasible path forward decisionsupport.

A key enabler of designing out preventable variation and to makingallowances for residual normal variation is the time constant to detectchange and make decisions. The decisioning time constant is enabled viathe disclosed method and system to fall within the time constant ofunrecoverable process oscillation.

Major sources of endogenous variation resulting from interdependenciesamongst resources are, for an exemplary embodiment in the perioperativeenvironment, delayed assets of the process due to unanticipatedcomplexities of interdependent activities, equipment breakdown,incomplete preparation, staff sickness, patients not biometricallyresponding to procedures, operating complexities and the like.

Certain embodiments include Duration Estimator, Block AllocationPlanner, Day View, Day Planner/Day Replay and Provider/Patient Kiosk aselements for information and decisioning designed to reduce systemicvariations by eliminating schedule risk up front and enabling rapidon-the-fly response to unanticipated perturbations to the clinicalprocess, for example.

It is advantageous to not only know what is scheduled, but to know thatwhat is scheduled is robust to unplanned events, to have an assessmentof the schedule risk, to have the ability to visualize likely processscenarios before hand, to be able to determine robust decisions that canbe taken in the dynamic environment that will best achieve the operatingobjectives, to learn from what transpired, to achieve departmentalobjectives and to include stakeholders in the clinical process. Each ofthese advantageous capabilities is advanced with certain embodiments ofthe present invention.

Referring to FIG. 1, a representative illustration of how variationimpacts scheduling is shown. Typically the average time for a givenprocedure is subject to variation due to a variety of reasons. However,a healthcare institution or hospital schedules procedures based onaverage time duration per procedure. Therefore, if a number ofprocedures take longer than expected then the remainder of scheduledprocedures may be delayed or rescheduled. Conversely, if procedures takeless time than expected then there is a risk of unused availability.Both underutilization and scheduling delays are costly in time andresources.

Clinical procedures have vast variation yet the advanced schedulingtypically is made at a single prescribed duration time. When procedurestake less than the forecast (typically estimated in the prior art as theaverage for a procedure plus, perhaps, a safety margin), valuable assetsare underutilized and clinical flexibility is degraded because theavailability is unanticipated/unactionable and further, downstreamprocesses are impacted from patient handling, staffing and equipmentturn around. When procedures take more time than forecasted, valuableassets are unavailable for other procedures that were scheduled thusbacking up upstream processes, creating staffing shortfalls and fatiguestress from the emotional responses to schedule collapse and recovery.

Estimating the duration of a procedure has a number of difficulties. Oneforecast difficulty is the variation on single procedure cases thatresult from doctor/staff combinations, patient disease state severity,patient medical complications and physical attributes such asweight/height/body mass index (BMI)/surface area, informationavailability, equipment and supply availability, day of week, procedureprior and etc. Another still is wrong or non-recorded information suchas, for example, time durations, and multiple procedures conducted withonly a subset being recorded as actually having been conducted.

The objective of understanding the variance in procedure duration is sothat the schedules may be made more robust. How schedules are maderobust will be discussed below with reference to a module referred to asDay Planner. It can be understood what the interactions of factors areon specific procedure combinations or alternatively, to classifycombinations of factors into defined duration probability densities.Either the procedure is called in a schedule planning simulation orusing the time density functions directly, for example.

Referring to FIG. 2, an embodiment for a Duration Estimator module isshown. Duration estimation is accomplished depending upon theinformation initially available, for example a history of procedures 24,and then a learning loop 23 is implemented that reduces the forecasterror by incorporating additional information such as accurate casetimes and well measured descriptive attributes that serve as leadingindicators. En an embodiment of duration estimation as shown in FIG. 2,a procedure is scheduled with average known time and variance at step 21for a procedure, e.g. in the operating room (OR) at step 22.Additionally, the history 24, like attributes 25, and variationexplained at steps 26 and 27 are then incorporated into forecasting andscheduling.

In the absence of a historical record, duration estimation is achievedvia expert input (not shown separately but can be included in history24). Experts include consensus of relevant professionals collated viaindustry working groups, societies and academic study, nursing,administrators, anesthesiologists and surgeons. Historical data ofrecorded procedures and their duration are more desirous than strictlyexpert opinion. A preliminary analytical step is to characterize theaccuracy (mean and statistical variation) of historical procedureduration vs. actual for like cases. This is achieved with a design ofexperiments on a subset of procedure and/or procedure clustering andthen compared to the historical record. Additionally, audits areconducted to compare scheduled vs. recorded as being conducted. Art fromthe methods of gauge repeatability and reliability are then used tocharacterize the confidence interval of forecasted duration. Regardlessof the accuracy, a measure of duration classification is made and itsdegree of uncertainty is established.

FIG. 1 depicts a concept reflected in Duration Estimation. Earlier thananticipated finishes may afford additional schedule margin if onlyvisibility existed either at the time of booking or once theprocedure(s) began that a room would have capacity. Likewise, delaysascertainable at scheduling or during the procedure, if betterunderstood, would impact the interdependencies downstream of theprocedure both for that clustering of patient/staff/asset and for otherimpacted clusters. The schedule robustness is attained from planning themedian, average and a notion of anticipated variation such as standarddeviation. Within this window 10, indicated as a dotted line, andadjustable and/or tunable via stochastic optimization, the system levelschedule is made to be robust (having a selectable and tunableprobability expectation).

The history of procedures is combined into a relational database orspreadsheet or analytical platform capable of data mining with dataincorporated into a data repository. The objective is to regresspotential leading indicators against the historical duration times in anattempt to incorporate significant predictive variables that can then beused at the time of booking to allocate time.

Where it is possible to describe variation with enough confidence, thenthe regression equation is utilized plus a fraction of the variation toset the duration. In calculating what is ‘enough confidence’, a fractionof the variation is included in addition to the calculated durationusing the regression equation.

Enough confidence is a function of the case throughput for a period,typically defined as a day given that there is a slack time off hoursfor schedule catch-ups to be made day to day. The cumulative probabilitydensity function for all cases in an allocated day is set to a level ofschedule risk at a given time within the day. This would be expressed asa desire to be, for example, that the caseload can be processed by 7 PMwith 90% confidence. A back propagation to the discrete cases is madesuch that the individual error terms (variation) are incorporated to thecalculated duration in fractional steps until such time as the portfolioof cases can meet the daily throughput/risk setpoints. This may beperformed manually or with the assistance of a stochastic optimization.

It can be the case that historical data is insufficient to separatebetween procedure(s) with statistical significance given the availableleading indicators. In this instance, the histogram of all procedurescan be partitioned and procedures that cannot be statistically separatedmay be clustered such that each is associated with a probability densityfunction (PDF) of time duration. The total of the PDFs form the completeenumeration of the historical procedures less holdouts of known datadefects.

A learning loop 23 is established that finely records attributes andprocedure times. As is the case when first classifying duration from thehistorical record, techniques including Artificial Neural Networks,Multivariate Regression, Analysis of Variance (ANOVA) and CorrelationAnalysis are used to refine predictive capability and tighten theconfidence bounds. Additionally new descriptive attributes may beappended in order to test and improve forecast accuracy.

Referring now to FIG. 3, an embodiment for a Block Allocation Plannermodule 30 is shown. Healthcare stakeholders need a cadence to whichschedules for room, staff, equipment and patients can be scheduled intoin such a way that achieves the provider's mission and operatingobjectives. Procedures are booked into the available blocks. Thisprocess is well described in the literature with a number of commercialapplications serving this function.

What does not exist is a method and system to allocate block time insuch a way as to simultaneously satisfy stakeholder preferences.Additionally, there is a present need to have available in the market anengine to ‘re-sort’ block allocation from time to time in aninstitution. Similar to the need for a defragmenter utility for acomputer hard drive—healthcare blocks are allocated based upon past,evolutionary activity. Staff, rooms, procedures, patient demographics,clinical specialties and competitors all drive changing assumptions offormer time and space allocations.

It is to be appreciated that methods and systems described herein mayenable increased throughput, schedule risk reduction, incentive drivenpolicy, preferences and a consistent environment. Hospitals, surgeons,patients and staff are beneficiaries.

The method proceeds as depicted in FIG. 3 showing the Block Allocationmodule. At step 32, input data such as utilization by surgeons, groupseligible for block time and time, room and staff preferences areprovided. As used herein, the term “block” refers to an allotted timeframe such as for scheduling an operating room or clinical procedureroom, e.g. imaging room. Historical block allocation data ischaracterized by the art of data mining to describe utilization bysurgeon. Hospital administration determines the groups eligible forblock time and preliminarily reconciles requests. The preferences areidentified both by historical check and by communication to thestakeholders—between surgeons and nurses, surgeons and rooms, equipmentand precedent/antecedent procedures, anesthesiologists and otherlogistical requests. Administration sets objectives such as the degreeof concurrent satisfaction of the preferences, departmental goals suchas throughput, case mix, doctor mix and departmental outcomes. Amulti-criteria optimization is run at step 33. At step 34, staff issubsequently assigned to operating rooms.

Objective decision-making is an explicit benefit of the disclosedsystem. Having the ability to communicate visually is key to rapidunderstanding. A ‘cockpit’ describing the historical fact patterns andcurrent operating results is key to facilitating conversation. The useof cockpits has been described in other known business systems. Afurther extension is depicted in FIG. 4.

The block schedule output may take many forms. An example embodiment isFIG. 5.

In certain embodiments of the present invention, a user interface modulemay be provided in order to get information to a user for use inscheduling procedures. In an exemplary embodiment, user interfacecomprise the ability to monitor activities for a given day, and will bereferred herein as “Day View”. Day View may be thought of as being aradar for the perioperative environment coupled to a logic engine. Morethat simply alarming a problem, Day View prospectively assessesinterdependencies such as, for example, a certain procedure and a pieceof equipment and staff required for the procedure. Should the locationand availability not be per the schedule, an alarm is first made as awarning for action. If as the time of need approaches, theinterdependent items are located via passive and active identification.If the issue can be resolved such as via a phone call or quick action—anissue was avoided or minimized.

Should the interdependency not be solved, Day View has the capability tosimultaneously solve for all other interdependencies and allow a‘what-if’ scenario testing exercise to occur. The what-if may be manualor automatic.

By having a procedure finish before the schedule, potential schedulingflexibility is lost for other issues that inevitably arise during theday such as staff availability, rooms and equipment constraints.Potential throughput capacity might also be impacted in that over thecourse of a day, one or more additional procedures could be insertedinto the schedule.

A longer than anticipated procedure can have the effect of backing upother dependent procedures for a particular patient either in theoriginal department or cross departmentally. Additionally, the rooms andassets that were originally allocated are no longer as available andtherefore other patients and activities are negatively impacted. Theremay be unscheduled overtime of staff or extra costs associated withturning around apparatus. New schedules must be made that impactsreworking of staff planning as well as the potential for rework on casepreparation for following or interdependent procedures. The anticipatedand/or needed throughput capacity may also be impacted negatively. Thesechanges reduce operating flexibility and increase the anxiety of theclinical process stakeholders—often leading to what is commonly known as‘burn-out’ and attrition, as well as to the potential ultimate qualityof delivered healthcare.

Referring now to FIG. 6, a block diagram illustration of a system foruse in planning clinical procedures in accordance with embodiments ofthe present invention. System 100 includes a duration estimator module,110, block allocation planner module 120 and user interface 130 asdescribed in detail above. While embodiments have been described withreference to a clinical setting, it is to be appreciated that otherenvironments may also benefit from similar methods and modules describedherein.

Certain embodiments provide a proactive application looking ahead atprocedure scheduling and duration to avoid delays by triggering anadvanced warning with sufficient time to respond in an event thatscheduled procedures will start or end before or after their scheduledtime. Certain embodiments provide recommendation regarding one or morespecific decision(s) or action(s) can be taken to add, drop or movespecific cases, task and assets, for example.

In certain embodiments, scheduling is provided that accounts foravailable personnel, available physical space, and available blocks oftime, for example. Certain embodiments account for variations associatedwith fluctuating demand and asset availability.

In certain embodiments, multiple criteria may be set for a schedulingprocess. Additionally, various risks associated with selected criteriamay be taken into account. Furthermore, rather than providing a singleschedule that is designed with average or generic or judgmental timebuffers to compensate for variation, certain embodiments enable aplurality of scheduling scenarios to be manually or dynamically enteredor simulated automatically to explore an available solution space andramifications on current and future activities. Certain embodimentsprovide suggested decisions calculated to help meet one or more static,dynamic or path-dependent configurable objectives.

In certain embodiments, a scheduler provides a capability to view asingle or multiple process metric, agent, and/or asset of the process aswell as a dynamic impact on interdependencies such that one or morecauses of variation in that process may be explicitly communicated andunderstood. Certain embodiments enable a dynamic view to process riskalong one or more dimensions in real time (or substantially real timewith some inherent system delay) or historically, for example.Additionally, certain embodiments may prompt an alarm, action and/orwarning if a certain process variable trips a threshold set point, forexample.

In certain embodiments, multiple simulation modalities are employedincluding a critical path method coupled to discrete event, agent, MonteCarlo and/or continuous simulation. Using this coupling, one or moreobjectives of the process may be assessed.

In certain embodiments, Day View or other user scheduler interfaceprovides features for historical review such as replaying a day or pastseveral days in order to extract from and discover process dynamics,training, and knowledge capture for future use as well as foradministrative activity cost(s), protocol verification and billing, forexample.

Utilization and asset consumption may be viewed to help understand astate of dynamic interdependencies between scheduling processes and tohelp determine which a schedule is likely to be met. If a determinationis made that a schedule is not likely to be met, data may be viewedand/or used to help identify what assets and interdependencies arecauses of schedule variance.

A future schedule view may be provided to calculate “what-if” scenariotesting to help understand schedule changes and effects of endogenousvariation, such as schedule adds or drops, resource availability,unforeseen delays or failures, etc. Future schedule extrapolation helpsto enable a higher process entitlement via better decisions thatdirectly and indirectly affect variation and throughput, wait times,stocks, capability and uses of assets in procedure and resourcescheduling.

Thus certain embodiments provide scheduling of processes in highlydynamic environments where knowledge workers and service providers areintegral agents of the process, rather than providing singular ordiscrete schedules with an objective and buffers allocated by judgmentor heuristics alone.

While many processes benefit from embodiments disclosed herein, anexample embodiment involving a hospital perioperative clinicalhealthcare delivery process is used for purposes of illustration only.

In an exemplary clinical process, a schedule may be derived amongavailable assets with well-understood interdependencies. For example, adoctor cannot be in two locations at once, and a surgery may notprogress unless, concurrently, the appropriate patient, doctors, nurses,information and equipment are in an operating environment suited for theprocedure. Additionally, the biometrical state of the patient should beadequate for a surgery to proceed.

FIG. 7 illustrates the Day View system in its process context accordingto an embodiment of the present invention. FIG. 7 illustrates task(s)results in creation of the day's first generation of a schedule, forexample. This schedule is then dynamically modified as endogenous andexogenous factors change. The schedules as delivered at the start of theday and as they change during the day have risk. Risk being defined hereas the chances of not making the schedule. Day View provides systems andmethods for estimating this risk and establishing corresponding alarmset points to minimize risks.

As shown in the flow of FIG. 7, Day View may estimate durations from ahistorical book or record of business. If no historical data exists,data from other related facilities may be used, for example. In certainembodiments, users may subscribe to services to receive or exchange datato aid in duration estimation and other calculation, for example.Additionally, access to other user data may allow comparison ofprocedure times between users/institutions, for example. After durationestimation, block allocation occurs. Then, interdependencies (e.g., onex-ray machine needed in two rooms; people, surgeons, instruments, etc.,needed in multiple places/times; etc.) are planned into the schedule.Then, Day View monitors activity as the day progresses in order to add,drop and/or otherwise intervene in a schedule with automated adjustmentand/or decision support. Other input, such as electronic medical record(EMR) systems, healthcare information systems (HIS), status/monitoringsystems (e.g., radio frequency identification, RFID, patient callsystems, patient bed monitoring, clinical systems and etc), opticalrecognition for shape of instrument/operating room activity, devices(e.g., electrocardiogram (EKG), anesthesiology, etc.), interaction withother processes, manual observations, staffing/equipment availability,may feed into Day View for correlation with a schedule or protocol.

In certain embodiments, a determination of an initial view of a schedulemay be prefaced by a sequence of analytical work. Referring to FIG. 7,activity durations 700 are utilized to schedule time within availablelimits. In the example embodiment, blocks of time 701 are defined withinwhich procedures may be booked for or by those entitled to provideperioperative service.

Responsible scheduling is considered to include an estimate of durationand a block of time allocation within which the procedure is consideredlikely to finish. While under-scheduling procedure time creates delaysin subsequent procedure starts, over-estimating and blocking time foravailable assets may create under-utilized capacity.

When average duration forecasts are used in a clinical environment, andeach room is considered unique outside the context of the staff's beforeand after tasks, chaos often results from concurrent demands. Utilizingprobability density functions 750 of time for a given durationestimation of a surgery is a foundation for calculating a schedule'srisk and system level performance or optimization, for example.

Referring to FIG. 7, a probability density function (PDF) of time 752for a procedure is calculated, typically from historical records ofsimilar procedures. The historical frequencies, in histogram form, forexample, are normalized by one or more standard statistical techniquesto create the PDF with area=1. Certain embodiments record informationincluding actual procedure code(s), time(s), staff patient specifics andprocess environment 730, for example.

A schedule risk is calculated by integrating the PDF using wellunderstood statistical techniques to create a cumulative probabilitydensity function for duration CPDF 760 where a probability of durationis made available. Given sufficient time 763, there is a 100% chance ofthe forecast duration matching the actual procedure duration. Likewise,given no time 762 there would be no probability of completing thesurgery within the time interval. The expected time to complete similartasks are found at the 50% probability 764 and is T_(expected) 761,which is the time where it is 50% likely the procedure will finishsooner and 50% likely that it will take longer than this time value. Thescale of time zero 762 and maximum 763 is continuous and directlyrelated to probability zero 766 to 100% 765 according to the PDFcumulative probability density function transform.

A risk that a procedure will not finish within its allotted time isdetermined by a probability 760 at the time allocation scheduled for theprocedure. Again referring to FIG. 7, the block of time reserved 740 isconceptually superimposed upon the PDF and CPDF aligned at time equalzero 741, 753, 762 with the same time scale such that the maximum time743, 751, 763 are aligned. For any selected block of time allocated 742,the probability of the actual procedure completing at or before thattime is found from the CPDF 760. The allocated time 744 is a decisionvariable available to the scheduler and/or optimization algorithm usedto achieve the schedule's objectives.

A lower specification limit, LSL 745, and upper specification limit, USL746, are similarly available. The LSL 745 is a minimum probabilitythreshold (the most schedule risk) of completing the procedure withinthe allocated time. The USL 746 is a degree of what is typicallyreferred to as “cushion” or “margin” in that it is the adjustablesurplus probability over an expected duration probability 764. The LSL745 and USL 746 are adjustable parameters and may be used as logicalrules in decision support 715 available to manual or automatedscheduling. The set points may be tuned in a learning feedback loop thatuses observed historical process data 862, learns probability set points742, 745, 746, 761 to optimize throughput and cost and offers optimizedtime allocation and probabilistic set point decision support.

Certain embodiments facilitate dynamic, intelligent schedule changebased on changes in the actual stochastic and interdependent processesof care occurring in the hospital. This method requires precastdurations of procedures arranged within a schedule along withinterdependencies of space, people, equipment, consumables andinformation (e.g., 700, 701, 702, 703). Actual process feedback isprovided such as from HIS, RFID, Optical recognition, telemetry andvarious clinical systems. An explicit mapping of interdependencies inprocess assets, such as those exemplified in FIG. 10 and their relatedtask probabilistic durations of activities is coupled to the system'ssimulation capability for finding feasible solutions.

Examples of events necessitating modification to the schedule 703include staff and equipment unavailability 738, upstream or downstreamprocesses not able to provide or receive patients 736, devices needed inthe scheduled tasks not functioning 735, people and equipment not inplanned location 733, 734, inputs from clinical or administrativesystems not adequate 732, patient biomedical adequacy or health statusnot within specification 731, added procedures 721 not in the schedule703, and dropped procedures 722 for any reason. The schedule may bemodified by changing assumptions 716 in the activities 700, 701, 702,703 used to create the schedule or dynamically managed in the Day Viewsystem 704. The changes to assumptions 716 may be manual or computergenerated to exploit feasible solutions.

In an environment involving many interdependencies, variation in anyinterdependent factor may propagate process disturbance. Certainembodiments help facilitate understanding and proactive management offactors that, if delayed or accelerated from plan, will likely increasethe probability of delay and preclude a process operating objective frombeing met.

Returning to FIG. 7, scheduling process interdependencies are analyzedat 702. As an illustrative example, a patient's vital biometricalreadings should be within surgical protocol specifications prior to theadministration of anesthesia. Surgery (and Surgeon) commencement isdependent upon anesthesiology, which, among other factors, is dependentupon the patient's biological readiness for the forthcomingprocedure(s). Certain embodiments of the present invention providemanagement of interdependencies in such a way that the appropriatefactors are given action if those factors, left unmanaged or alongcurrent trajectory, would delay the schedule or diminish the processobjectives. For example, in the illustration, it may be that the surgeonis required to be scrubbed in at the time of anesthesiology. However,given the patient's biometrical state, the surgeon is not actually animpediment to starting surgery. Thus, factors affecting biometrics areilluminated as important tasks, and interdependent people, equipment,space, and information are given an estimated time before returning tocritical path.

FIG. 8 illustrates an exemplary setup and use of the Day View system inaccordance with an embodiment of the present invention. Building blocksof information which allow calculation of schedule risk, an ability toproactively manage root cause variation and produce historical “whatwas” emulation, “what is” visualization, “what will be” simulation, and“what if” simulated scenario generation are provided. In certainembodiments, the method may be used to compare protocol requirements tothe actual or generated schedule.

In accordance with FIG. 8, the Day View system is set up for the day byselecting protocol(s) that are supposed to happen, loading a schedule toexecute the protocols based upon a patient's care plan, selectingdecision rules to resolve interdependencies, and selecting probabledurations, for example. Previously-unscheduled surgical cases that needto happen that day are added and dropped procedures are removed. Oncethe day commences, a state of assets, personnel, equipment, rooms, etc.,are monitored to determine their status. Dynamic metrics and variationin clinical processes are determined during the day. As the dayprogresses, schedule risk is reiteratively calculated, and adetermination is made to adjust the schedule. Using Day View, a systemand/or user can see what has happened, what is happening, what willlikely happen given what we know, and can simulate what-if scenarios.The system and/or user can then minimize underutilized capacity, reducedelay or improve patient, doctor, nurse allocation by adjustingscheduling.

The dynamic Day View 800 is constructed via the execution of analyticaltasks as depicted in FIG. 8 in accordance with an embodiment of thepresent invention. The disclosed analytical tasks 800 may be performedmanually. However, the Day View system enables these tasks 800 with anelectronic workflow that extracts, transforms and loads data and thenexecutes algorithms in sequence. Relevant algorithms may be executedusing a method and system disclosed by Akbay and Alkemper known asDecision Execution System, via hard-coded computer logic, and/or viaconfiguration of commercial Extract-Transform-Load and workflow tools,for example.

As illustrated in FIG. 8, an initial schedule view 806 is establishedwith, for example, four building blocks 801, 802, 803, and 804. Aninitial schedule 801, 703 is obtained to provide a scheduled time,place, activity, based upon the care plan protocols required and therelated assets (e.g., FIG. 10) of the process. Second, a clinicalprotocol 802 of the procedure is identified. A protocol establishes whatassets and procedures make up a process, for example.

General agreement should exist between the schedule 801 and protocol802. Referring to FIG. 8, schedule-protocol exceptions are identified bycomparing a count of requisite assets in a structured way 890. Ataxonomy of standardized asset names 891 is populated with the count ofscheduled assets 703, 801, 892 and those called for by protocol 802 foreach category called for by the taxonomy 891. Where the number of assetsmatches, there is no exception. This is illustrated by line item 895,for example. Where the number of specific assets called for by protocol893 exceeds those that are scheduled 892, an exception 897 is madeapparent. Certain embodiments provide configurability as to how theexception is reported: scheduled to protocol or visa versa 899, forexample. Alternatively, there may be more assets scheduled thanrequested by protocol 898, and that is illuminated.

A third of four uploads of data to establish the initial schedule DayView includes a Decision Support rule set 803. The rule set 803encapsulates the logic specific to protocol and process. An illustrativeexample relates to a specialized surgical tool. A sterilized probeshould be available in a surgical case cart. If a probe is notavailable, the surgery may proceed if there are two probes on standby onthe preoperative floor. Logic 803, 715 may be rule-based, example-based,evidential reasoning, fuzzy logic-based, case-based, and/or otherartificial intelligence-based logic, for example.

A fourth upload of data is the duration estimation PDF portfolio.Alternatively, rather than replicate an instance of this knowledge base804, the analytical workflow 805 may have access to the DurationClassification Engine 700 deployed in the scheduling operation.

Known additions and deletions 807 are made to the initial schedule 806.Illustrative examples include a request to add an emergency surgery to aday, a staff person calls in sick, etc. Timing of this activity 807 hasmaximum utility if done before the start of scheduled activities andsufficiently in advance to allow time for staff review of the upcomingtasks 808.

A review of an upcoming period's process task, schedule risks andcontingency plans is beneficial to provide a contextual understanding ofactivities as well as to solicit opinions of staff to then modify theschedule for improvement. It may also be beneficial for individual andteam preparation and training, for example. At any point, schedule tasksand assets may be simulated forward. Utility may be gained in shiftchange handovers, for example.

At the start of scheduled activities, variations may begin to occur.Certain embodiments of the present invention help to manage variationsystemically in such a way as to meet the objective of a schedulewithout needless non-value-added activity, such as efforts to completeactions at a rate impacting a critical path for a schedule.

Day View manages a scheduling critical path by monitoring factors thatwill impact the critical path. If scheduling assets are in designatedlocation(s) and state(s) of availability, a schedule can execute asplanned. If a state of an asset in a process or an activity of theprocess is delayed or accelerated from its designated time, place andstate, a chain of dependencies may be impacted. Day View instantaneouslycalculates durations and the scheduled risks 706 and alarms atprobabilistic set points 811, 813, 815.

Asset states 810 are relevant degrees to which an asset is available orbiometrical states of a patient. As an illustrative example, a certainpiece of equipment is an asset in a schedule process, and a desiredstate of that asset at a beginning of a designated surgical procedure isto be calibrated and sterile. The state of the equipment is according toplan if each of the requirements are met. The state of the monitoredassets 810 is dynamically assessed, for example.

Asset location 820 is monitored as well. For example, Day Viewdetermines the process implication of an asset's availability at adesignated location at a designated time. Extending the example used inasset state 810, a specialized piece of equipment required for a surgerymust be in the operating room. A Boolean indicator of whether an assetis or is not in location as well as a degree or rate of being in thedesignated location in a future time may be provided, for example.

An illustrative example is an operation beginning in one hour.Specialized equipment is required for the operation at that time.However, the equipment is currently being sterilized. Based on timinginformation, the equipment's estimated time to sterilization is deemedsufficient to become sterilized and transported to the designated roomwithin an hour.

An asset's dynamic metrics 830 may also be used by the Day View system,for example. Examples of asset metrics include an oxygen saturation frompulse oximiters, status codes from equipment, consumption profilesdispensed in anesthesia delivery, etc. Day View utilizes thisinformation for availability and anticipatory forecasting, for example.An example of anticipatory forecasting is use of biometrical data froman operating room to assess progress relative to a protocol in order toassess a rate as in the asset location 820 illustrative examplediscussed above. For example, anticipatory forecasting may be used todetermine if a surgery with complete within the next hour.

A dynamic view of a process and status of interdependencies is provided840 with a comparison of what should be happening versus what ishappening, what is about to be happening, and/or what is scheduled tohappen, for example. Enabling assets that are missing through location,rate, and/or state are highlighted to process stakeholders in the formof ‘satisfactory’, ‘warning’ (pre-emptive action is required) or‘danger’ (schedule will not be met and alternations to it are required),for example. While “satisfactory” “warning’ and “danger” are utilized toillustrate the principle, other context appropriate designators may beused.

A plurality of modes may be used to view a scheduling process inaccordance with embodiments of the present invention. Four primaryviewing modes including “what-was”, “what-is”, “what-will-be”,“what-if”. “What-was” 850 is a replay of activities, interdependencesand risk levels as the process actually experienced. “What-is 860 is aview into what is happening in the process at the time of being viewedalong with the current interdependences, states of assets, people andprocess risk. “What will be” 870 is the process in the future if thetrending of “what-is” remains. “What-is” may be analogous to a radar ona vessel looking sufficiently far enough out over the horizon such thatcorrective action may be taken. “What-if” 880 is a scenario-based viewto aid decision support. Alternatively, “What-if” can be retrospectiveto evaluate the likely outcomes had a “what-if” decision been madeinstead of the actual historical one.

In each of the four views, an objective is to have better processoutcomes via reduction of controllable variation. Decision support maybe provided to process stakeholders through the views, for example.

An analytical roadmap or workflow to build decision support using DayView is depicted in FIG. 9. A Day View is constructed from, for example,eight analytical steps which begin with identification of task(s) andasset(s) of process interdependencies; measures of availability;calculation of critical path; and use of exogenous variation,probabilistic duration, availability and reliability to calculate viasimulation or math programming a probability of events beginning andending at scheduled times or conversely, the start and complete times toachieve a designated probability estimate, for example. Assetinterdependencies and dependency state design may determined along withcritical path calculations. Variation in items such as reliability ofequipment, attendance record of staff, etc. may be factored in alongwith a probability duration estimate to determine a base schedule. Thebase schedule may then be simulated with a schedule risk. Decisionsupport may be initiated to intervene in the process.

Key interdependencies are identified around consumption and utilizationof assets (see, e.g., FIG. 10) in a process. These interdependencies arecaptured using a critical path method (CPM) transfer function technique901. Critical paths are calculated using method known in the area ofmanagement sciences. Critical path and process slack times are madeexplicit via calculation and display, for example.

For assets most likely to become on critical path and potentially delayor cause bottlenecks in a critical path within the forecast horizon,monitoring is enabled 904 along metrics to measure availability,readiness and state, for example. An actual critical path is calculated906 knowing the structure of interdependencies and state of the assetsin the process using Gantt and CPM methods, for example.

Certain embodiments help provide a degree of robustness or tolerance ofa schedule to variation. PDF's of endogenous variation from equipmentavailability and reliability of staffing and patients 908 are used withprobabilistic duration estimates 910 and exogenous variation 920, suchas adds and actual or likely drops to the schedule as well as historicalvolatility (e.g., factoring for seasonal variation and capacity) areused in a multi-modality simulation 940 to calculate durationprobabilities of scheduled tasks being completed. The simulation engine940 is repurposed for decision support to run “what-if” scenarios, forexample.

FIG. 10 illustrates exemplary assets capable of use in a Day Viewscheduling process according to an embodiment of the present invention.Assets are items of value that can be used by the process. Assetsinclude people, time and place, information, consumables, equipment andcapital, for example. Assets can be used or consumed. Assets typicallyhave availability and often reliability variation. In certainembodiments, asset availability and reliability are estimated initiallyfrom judgment and then from actual history. Probabilities foravailability and reliability are then calculated for a new or forecastedcontext using (from history) observations made from a similar context ata singular operation or from a plurality of institutions.

FIG. 11 illustrates process management and value according to anembodiment of the present invention. Delivery of healthcare services isused for purposes of illustration only. Hospital location andperioperative services are used to describe features, although the venuecan be any clinical venue such as another department in the hospital, anambulatory clinic, doctor's office or ward set up on a temporary basissuch as for emergency or conflict. A cohort may be one or multipleinstitutions. A venue can be in domains outside of healthcare andinclude any ordered sequence of activities or use of assets thatcomprise process and schedule. An illustrative venue is a perioperativesuite with flow into and out of up and downstream processes. Certainassets of the process are used, and the relationships of probabilisticduration, availability and reliability are used to aid in theexplanation of methods used to calculate process risk.

In an exemplary embodiment, a clinical process (FIG. 11) is managedaccording to certain embodiments of the present invention. As shown inFIG. 11, operating rooms and other facilities do not exist in a vacuum.There are inputs and outputs to each scheduling process. Bottlenecks andvariation may be introduced because, for example, even though a surgeonmay be available for a surgery in one room, that room may be behindschedule due to post-op delay from a previous procedure or a lack ofbeds in recovery. Assets, durations and interdependencies may bereviewed to determine a cumulative probability distribution with allinterdependencies for the delay and associated probability ofcompletion. The schedule may then be stretched and/or shrunk to meetacceptable tradeoffs for necessarily operations during that day.

Patients arrive 1101 through an emergency department 1102, or walk in toreceive emergency or scheduled services 1103. Live patients aredischarged after clinical services are rendered 1109 and leave thehospital 1110. Within the clinical process, many departments provideclinical services and each benefit from Day View scheduling, forexample. For illustrative simplification, a clinical workflow in andsupporting a hospital preoperative department 1122 is considered.

Patients having surgery are either scheduled or are presented forsurgery in an emergency. Scheduling of surgical services is typicallymade between a surgeon's or physician's office and a hospital accordinga surgeon's allocated blocks of time 701. Surgical procedures arescheduled days, weeks or even months in advance. Emergency proceduresare not scheduled and, if reserve capacity is now available, emergencyadd-ons to a schedule constitute a significant source of variation. Itis not always possible to set aside reserve capacity.

Patients are prepared for surgery 1104. Sometimes, patients arebiometrically prepared for surgery and have documents or informationsuch as lab results in preparation for surgery prior to the scheduledstarting time. Examples of being prepared include being in a surgicalpreparation area (such as patients P1 and P2) and having blood pressure,heart rates, blood chemistries, and digestive chemistries withinprotocol and surgical permissions. Dependencies to be satisfied beforepreoperative surgical services can commence may be monitored. Forexample, if forms 1160 must be signed at least 15 minutes prior tosurgery, staff may be alerted with sufficient time to complete the taskprior to the schedule surgical start.

For illustrative purposes, suppose that a specialized piece of equipmentE1 1161 in operating room 1 (OR1) 1120 is needed in OR4 1137 in one hour1165, and E1 is needed in OR1 in 15 minutes 1161, or the OR1 surgerycannot commence. Likewise, the surgery schedule in OR4 in one hour and15 minutes also cannot start without E1 1165. Further, with respect to aprotocol step using E1 for patient P1, the person scheduled for surgeryin OR1 has a 65% probability of being finished within 45 minutes, an 80%probability of being done within 50 minutes and a 98% probability offinishing within 70 minutes 1162. Furthermore, surgeon S1 is scheduledto report to OR2 1130 for patient P2 in two hours 1164. If signature ofform F1 1160 is delayed, E1 1161 is on critical path because OR4 1137may start late, Should F1 not be signed until 08:15 hours 1165, OR4'sschedule risk is 35%, which is derived from knowing E1 is critical pathand its probability of being used and released in 45 minutes (theoriginal one hour minus 15 minutes delay) is 65% (1162), all otherfactors are satisfactory. If OR4 1137 is not on critical path, then thisdelay is an annoyance, but not critical as yet. However, note thatsurgeon S1 is scheduled to start OR2 1130 at 09:45 hours (1165), andthat if F1 1160 is not signed until 08:15 hours (1165), that F1 and S1are now critical path. The Day View Logic may generate a “yellow” alertto nurse N1 to review and have signed F1 at 07:45 hours, for example.The status of F1 would transition to ‘Red’ at 08:00 hours if the formsremain unsigned because of the S1 dependency on OR2 1164. As can beappreciated, combinations between numbers of rooms, patients, staff,equipment and information follow Powers Law.

Extending the illustration in FIG. 11, E2 1125 is a shared devicebetween OR1 1120 and OR2 1130. It should be located between the roomsand be operational. The Day View system monitors these dependencies 810,820, and converts protocol 802, rules 803 and schedule 8018 intocritical path logic 906. Variation simulated risk 930 is derived 1162.

Thus, in certain embodiments, a scheduling process includes a dynamicflow of people, assets and information. In the above example embodiment,once surgical procedures are rendered 1122, the clinical workflow movespatients into a post-operative (post-op) area 1150 for recovery andobservation/stabilization. Patients are then removed 1107 to floor beds1108 or discharged 1109, for example. If post-op 1150 or beds 1108 haveno capacity, then the dependent antecedent processes are impacted. DayView provides visibility to upstream and downstream processes using themethod illustrated above for OR dependencies, for example.

Shared services, such as nursing, surgical instrumentation, equipmenttechnologists and cleaning are scheduled and/or called-for tasks.Availability and capacity constraints impact dependent processes.Illustrative examples include a nurse calling in to be absent from anupcoming shift, a surgical case cart not correctly inventoried, or acleaning technician occupied in one room as another room is requiringclean-up. A ripple effect of these variations increases processvariation. This process variation increases as discrete randomnessaccumulates.

Day View provides not only a system for identifying causes and effectsof variation, but also a mechanism to help recover from unanticipatedvariation in a data driven and visual way, so as to facilitateconversation, ideas and buy-in of process stakeholders.

Day View may be configured to accommodate increasing degrees ofexogenous variation and process objectives. For example, the Day Viewsystem may be configured to accommodate cases added to an already fullschedule, as well as accommodate concurrently scheduled procedures thatfinish early and/or late. Process stakeholders help determine where toplace new cases, what cases to postpone, which staff to add, redirect ordrop, which rooms to shuffle, how to ration equipment, supplies, etc.

Certain embodiments use simulation to explore potential feasiblesolutions. For example, four modalities of simulation are employed toforecast asset and resource utilization assumptions. These fourexemplary modalities are agent based simulation (AB), discrete eventsimulation (DE), continuous or system dynamic simulation (SD), and MonteCarlo simulation (MC). A critical path method (CPM) may also berepurposed in a simulation-based mode in that CPM is used to calculatecritical path, probabilities of completion, and availability andfeasibility of a solution.

FIG. 12 illustrates dynamic system context and interaction between avariety of simulation and forecast modalities in accordance with anembodiment of the present invention. Different simulation and forecastmodalities may be utilized, for example. Critical path methods andforecast modalities may be utilized, for example. Methods such as CPM,DE, AB, MC, and/or continuous, differential or system SD, may be used.Historical observations may be organized in the form of a histogram andtransposed into a probability density function and a cumulativeprobability density function (PDF) for incorporation into the CPM logicand as assumption feedstock for the simulations, for example.Differences between DE and AB in terms of process structure being known(e.g., scheduled) with DE or emergent with AB are illustrated, forexample, in FIG. 12. How AB can be encapsulated by SD and the stock andflow of SD can be inferred from AB is illustrated. Using a number ofdifferent simulation modalities concurrently to solve forward-lookedaspects of a schedule is used to determine what will happen during theday, for example.

Referring to FIG. 12, an interaction of simulation modalities isdescribed. The ordering of tasks in a given schedule is not random, but,rather, is meant to extract maximum or improved utility from assets of aprocess to achieve one or more process objective(s). The CPM 1201 is onesuch method to identify and organize interdependencies in a way thatenables feasible solutions. The CPM, while novel in its application todynamic scheduling environments in healthcare operations, has drawbacksthat diminish its utility when used in isolation without the benefit ofthe methods disclosed herein. CPM assumes that tasks will be completedas scheduled. Practically, this is seldom the case in dynamicenvironments. Thus, the believed critical path is but one instance ofthousands of potential scenarios, some of which alter the tasksoriginally thought to be on critical path. A first extension of thecritical path method is to utilize multi-modality simulation to drawpath-independent probabilities or duration into the assumed tasklengths. Additionally, equipment and personnel availabilities areincorporated as well.

The presently disclosed methods and systems are differentiated fromGantt and Program Evaluation and Review Techniques (PERTs) that replacedeterministic duration assumptions in CPM with task durationprobabilities. Certain embodiments not only draw from a staticassumption of a probability distribution function such as Gantt but alsoprovide duration probabilities from coupling to a simulation of thephysical environment. Thus the CPM or PERT method is enhanced with amost recent actual duration that is observed within the hospital's orother clinical environment's operations from protocols that are tied tothe specific care pathways for each patient and also an added precisionof dynamic forecasts of duration that result from incorporation ofprocess signals and activities on a dynamic basis into probabilityassumptions feeding CPM/PERT.

For illustrative purposes, Task1 1202 is, on average, 1 hour in lengthand completes with 95% probability in 90 minutes. The current artutilizes a single number for duration, typically, the mean or mode.Therefore, nearly half of the time an early finish is realized and halftime, a delay is caused. If the delay is a critical path item, otherprocedures, staff, assets and patients are negatively impacted.

A schedule risk is estimated by converting historical observation ofsimilar task and context durations 1241 into a PDF 1242 and integratinginto a cumulative PDF 1243 using one or several simulation methods 1240,for example. Returning to the CPM for Task1 1202, task and totalschedule risk are calculated along a full enumeration of pathindependent values for time assumptions of tasks. The method allows foran assessment of a given schedule's likelihood of completing onschedule, as well as for any selected time or probability. In this way,a schedule may be adjusted to complete with “50% probability”, “80%probability”, “95% probability”, and/or any other desired likelihood.

Alternatively, a schedule can be made, and a likelihood of any taskbeing completed at a given time can be expressed in a probability, suchas 0R3 will be ready for surgery at 1430 hours with 85% probability andat 1500 hours with 95% probability.

Tasks are interdependent on people and assets. Path independentassumptions, though superior to static scheduling, may not be sufficientto determine if a given person will in fact be available. An independentassumption assumes that resources required for a task will be available.Certain embodiments of the CPM method disclosed herein call assets of aprocess as though the assets were tasks and triggers alarms accordinglyif double booking or a selected critical path risk is exceeded. However,certain embodiments employ more specific treatment of resources requiredfor tasks through use of discrete event and agent based simulation.

Discrete event simulation 1210 is an ordered step-through of determinedtasks 1212, 1213 in discrete time increments. At each time step,designated resources used for the task(s) at hand are attached 1211,1214. Should concurrent tasks involve the same resource(s), logic 1215is called from the simulation to determine which task has priority andwhich release rules apply once a particular resource is attached to aparticular task. Thus, it may be possible that a single resource canserve two or more tasks and not be mutually exclusive as the way CPM isbroadly configured.

Agent-based simulation 1220 assumes that the resources 1221, 1222contain a prioritization rule set and respond to the surroundingenvironment. By studying how the resources (agents) respond, a structureof the system emerges. This is in contrast to Monte Carlo 1240 that isconfigured with a path independent assumption and linear or formulaiccorrelation that assumes the simulated task is much the same as itshistorical assumption set 1241. Monte Carlo determines a systemstructure from historical assumptions and from discrete events inordered tasks and logical call of assets or resources.

Dynamic processes with interdependencies, randomness and human judgmentdefy any singular simulation modality. Certain embodiments utilize amulti-modality approach to asset utilization so that a schedulingstructure enabled with the CPM method can be made more accurate and yetpreserve the benefits of the visual logic CPM 1201 communicating in anintuitive, rapid, systemic form.

Continuous or system dynamic simulation 1227 is yet a fourth modalityand is employed by the Day View scheduling system to assess “stocks”,“flows” and “feedback structure” dynamics in order to assess processtendencies and policy impact. While process objectives are often relatedto capacity, throughput and timeliness, there are also strategicobjectives that are often met or missed—not in a single shift, day orweek, but over time. Examples include staff skill, “burnout”, staffturnover, reputation, capacity to serve, and financial operating margin.This class of objectives share the property of accumulation. Forexample, “skill” is a stock that may increase with exposure to differentand/or frequent procedures. “Skill” may also erode through memory and/ordexterity loss from infrequent exposure or training. Similarly, aclinical workflow policy that schedules finish times at an expectedvalue (the 50^(th) percentile) will result in near daily, if not hourlyschedule misses and, resultantly, a disruptive, chaotic, stressful workday for the process stakeholders.

Occasional days of chaos are typical in all domains wherein schedulingexists. Related stress from schedule chaos, unanticipated variation,emotional decision making, unplanned overtime and the like willaccumulate and, for some staff, will exceed a preference or indifferencepoint causing the staff to seek other employment. A dynamic of new hiresis that they do not have departmental knowledge, relationships and trustbuilt due to a lack of history, context and/or requisite competencies.Existing staff are then encumbered with training the new staff. Thusthere is detraction from a department's ability to execute tasks whenexperienced staff quit and new (ongoing) replacements enter. Processvariation increases with lower capability if schedule durations remainas before. Thus, other process objectives are desirable to attain andcan be attained by policies and decisions executed on an hourly, daily,and/or weekly basis via the decisions made in the clinical workflowenabled with Day View.

FIG. 13 provides an economic, rather than throughput, view of ascheduled environment. A scheduling environment is a dynamic structure.An operator seeks to break even or have an account to cover a loss inoperating margin 1301, for example. Assuming a margin exists, part ofoperating margin 1301 may be invested in staff and other assets.Hopefully, more margins grow results, but asset/staff investment canoutstrip an ability to gain additional margin. Day View economicanalysis may help to show a facility is not losing money by getting abetter asset. Staff and equipment capability may be balanced with volumeof patients/procedures to be handled. Variations may be caused by staffturnover, for example. Day View may be used to help accommodatevariations without destroying any subsystem of the main system orburning out personnel, for example.

Referring to FIG. 13, an example embodiment of an archetype structurefor macro objectives is presented according to an embodiment of thepresent invention. Beyond minute to minute, hourly, shift, daily,weekly, monthly, and/or other operational time constant within whichtasks of a process are scheduled and work, there are typicallyadditional process objectives to be managed and traded against as wellas throughput, inventory, operating expense and ability to fulfill.These objectives may comprise scheduling and scheduling risk managementmethods. These macro objectives include budget, asset and staffinvestments, such as, for example, capitalized equipment, consumablestock, physical plant, staffing levels, staff competence and recruitedstaff. Having actual capability, capacity, and cost structure allow moreeffective use of asset and staff investments. An ability to attract moreinputs into the system or adversely exclude entrants that the processsystem would not be advantageously suited to serve may be provided inDay View. Certain embodiments provide an ability to create economicvalue addition or an ability to meet financial targets, for example. Avirtuous cycle that creates re-enforcing dynamics has dynamic counterforces and limits to growth. Attracting more entrants into the systemthan the system can sustain with expected or required service levelsresults in staff burnout or poor process outcomes. Over investing incapability whose cost cannot be liquidated result in financial loss thatmay not be sustainable. Served markets may not have sufficient volumesto sustain entrants into the process system built with an operating (andcost) structure that is designed for more (or different) volume. Certainembodiments allow process stakeholders to utilize both immediate processdecisioning and policy and strategic decisions in such a way as to makeinformed decisions with probabilistic trade-offs.

Referring to FIG. 13, system dynamics simulation may be employed, andthe structure of the macro system is such that it is desirable tooperate a department with enough financial surplus or within budget thatthe department can improve itself, or at least maintain capacity andachieve effective outcomes. Operating margin 1301 is created by asurplus of revenues minus expenses. This margin may be invested in assetand staff 1305, paid to an institution, or deemed an allowed loss fromthe institution (wherein costs are netted out with a budget from theinstitution). Ideally, with asset and staff investment 1305, adepartment gains increased capability 1307, which would typically leadto capacity and reputation to attract more patients 1309 and thereforeincrease revenues, resulting in an increased operating margin 1301,assuming marginal revenues exceed marginal costs, for example. Anability to increase capability, lower cost and achieve more throughputcomes from reinvestment 1305, and the cycle continues 1320.

In practice, there are endogenous forces that limit perpetual growth1320. Should increased demand 1309 exceed capability 1307, a staff maybecome overextended or quit 1315, capability may diminish 1307, andfewer patients avail themselves of these services until demand abates toa level the staff can serve. Likewise, an overloaded staff and/or assetsreduce effectiveness 1312 of asset and staff investment 1305. Forexample, a specialized asset for a certain procedure is obtained 1305with intent to increase a department's capability 1307. However, staff,responding to process chaos, do not develop skill in the new competencyand thus, the depreciating asset's costs are not liquated by revenueoriginally intended to be garnered by the investment.

Referring to FIG. 14, an ability to play the day plan and/or scheduleforward, an ability to replay the day as it was scheduled and/or emulateit as it actually occurred, and an ability to view a summary indicatoras to the overall process state are explained. The assets of the processand their tasks 1402 are explicitly mapped as to theirinterdependencies, as has been discussed above. The relationshipsbetween tasks are not equal relative to their impact upon the schedule;some tasks are able to be the limiting factor in completing a scheduleon time while others are not as they do not have interdependencies. Task1404 is on the “critical path” 1406 while task 1408 is not. Theclustered set of activities associated with a subsection of the day,such as an operation scheduled for a given room, are managed intensely.One operating room 1422 may impact another 1424 operating room'sschedule in that task 1413 can not begin until task 1411 completes at1410 since the same surgeon, for example, is involved. The risk of alate start is calculated from the methods and systems already discussedabove and is visualized at 1412 for the attention of processstakeholders. The visualization may also be summary in nature, such as ared/yellow/green indicator or a “happy face”. The summary indicators,1426, 1427, 1428 enable the rapid scan of hundreds of activities suchthat the tasks involving intervention to keep to the schedule can beaddressed while the rest of the system remains on schedule. Thesetpoints 1432 that transition from one process summary state to anothercan be deterministic or probabilistic 1430. Deterministic settings arerule based such that if a schedule is now late or if a downstream tasksuch as 1413 to 1411 is to be impacted for a given duration, a happyface may turn from a smile 1426 to a frown 1430 to a sad face 1428depending upon the setpoints. Probabilistic summaries take betteradvantage of the present invention's analytical method in that ascheduled task in the future may not start because of the present delaysand/or the accumulated probabilities of delays building throughout theday. However, the future task may also start on time if things go wellbetween now and then. A CPDF transformation is used to trigger the statesummary and the transitions are adjustable 1432.

An ability to play the day forward is an improvement to the art ofservice-oriented processes. Prior to the start of a shift, for example,the day's schedule is advanced through time 1414 by adjusting at 1420the virtual time 1418, such as by a slider bar or dial, and watching theschedule unfold along with the relative risks of delays or earlycompletions of the assets of the process. In this way, the staff can becognizant of the sensitivities and key risks to the schedule and can beadditionally alert when risks increase to the key points. Likewise, aday can be replayed for study or training of decision supportalgorithms. The replay can be historical and/or comparative to what wasplanned or even what the scheduling algorithm chose as a robust pathforward but was not necessarily selected.

In certain embodiments, information may be provided using a kiosk orworkstation. Kiosk screens may be displayed with schedule, planningand/or decision support information, for example. A clinical workflowmay be facilitated using input and output provided at the kiosk. Kioskscreens and information may be used to driving a clinical schedulingprocess.

In a perioperative suite, for example, a whiteboard, one or morecomputer terminals (e.g., Microsoft Windows-based and/or green screencomputers), and/or one or more flat panel screens hung on the walldisplay activity in different rooms. Color may be used to show whetheror not operations are proceeding according to schedule. In certainembodiments, a decision support window may sit or pop up on top of anapplication running graphics on a screen.

Thus, certain embodiments provide systems and methods for usingprobabilities to schedule and modify schedules and clinical processes inhealthcare delivery. Certain embodiments provide systems and methods tocalculate the probability that a scheduled task will start at given atime using CPM and PERT with assumptions derived from an active processfeedback and probabilistic historical task durations. Certainembodiments provide systems and methods to calculate the probabilitythat a scheduled task will end at given a time using CPM and PERT withassumptions derived from an active process feedback and probabilistichistorical task durations. Certain embodiments provide systems andmethods to calculate the probability that a whole or any part of aschedule will start and/or complete as scheduled and/or a given time(s)using CPM and PERT with assumptions derived from an active processfeedback and probabilistic historical task durations.

Certain embodiments provide systems and methods to calculate the timethat scheduled task will start given a desired probability of a startoccurring using CPM and PERT in a simulation or static mode drawing uponassumptions derived from an active process feedback and probabilistichistorical task durations. Certain embodiments provide systems andmethods to calculate the time that a scheduled task will end given aprobability of an end occurring using CPM and PERT with assumptionsderived from an active process feedback and probabilistic historicaltask durations.

Certain embodiments provide systems and methods to calculate theprobability of meeting tasks in a schedule of healthcare clinicalprocesses using the critical path method (CPM) to identify tasks andinterdependencies that will be impacted by any other task or resource atany time in the schedule.

Certain embodiments provide systems and methods of using probabilisticdurations with the CPM and or PERT methods to calculate probabilities ofscheduled task starts of finishes and predicent and anticent tasks orresources (people, assets and information) in healthcare clinicalprocesses.

Certain embodiments provide systems and methods to use CPM or PERT tocalculate slack time for resources in the active processes of healthcaredelivery by embedding the algorithms of these methods into acomputerized information and decisioning system which actively takes inprocess and related information, calculates the algorithms and providesactive process decision support or automation at one or a multiple oflocations.

Certain embodiments provide systems and methods to warn processstakeholders of a deviation from schedule that will under or overutilize capacity of a process in advance of the scheduled time for thatprocess or resource using CPM and PERT with assumptions derived from anactive process feedback and probabilistic historical task durations andwill feed forward capacity ramifications to process stakeholders whomake decisions to add or drop surgical cases or manage beds,transportation and physical assets.

Certain embodiments provide systems and methods to incorporatedynamically updated probability distributions which benefit from thehistorical and current actual process durations at one or a plurality ofhospitals such that the assumptions gain ever more precision whereobservations at one hospital may be too infrequent into the CPM/PERTbased logic rather than a static probability assumptions to calculateprobabilities of scheduled task starts or finishes for tasks orresources (People, assets and information) in healthcare clinicalprocesses.

Certain embodiments provide systems and methods for illuminating variousstates of the schedule with visual, audible, tactile, numerical or shape(or any combination there of) context relative to the degree of impactif certain tasks are not done or resources made available.

Certain embodiments provide systems and methods to identify root causeof actual and likely schedule variation by a decision logic treeincorporating schedule risk calculated from a cumulative probabilitydensity function or closed form approximation and active process statusfeedback combined with Gantt, CPM or PERT algorithms in healthcaredelivery processes.

Certain embodiments provide systems and methods to identify whatresource(s) to focus on to change the schedule using a decision logictree incorporating schedule risk calculated from a cumulativeprobability density function or closed form approximation and activeprocess status feedback combined with Gantt, CPM or PERT algorithms inhealthcare delivery processes.

Certain embodiments include systems and methods providing use incombination of discrete event, agent based, continuous or systemdynamics simulation or Monte Carlo simulation integrated with PERT andCPM in healthcare related processes to simulate “what if” scenarios,calculate schedule risk, find feasible process solutions, replay eventsof historical record in an emulation and as a transfer function that canbe called from an optimization.

Certain embodiments include systems and methods providing use of agentbased simulation integrated with PERT and CPM in healthcare relatedprocesses to simulate “what if” scenarios, calculate schedule risk, findfeasible process solutions, replay events of historical record in anemulation and as a transfer function that can be called from anoptimization.

Certain embodiments include systems and methods providing use ofcontinuous or System Dynamics simulation integrated with PERT and CPM inhealthcare related processes to simulate “what if” scenarios, calculateschedule risk, find feasible process solutions, replay events ofhistorical record in an emulation and as a transfer function that can becalled from an optimization.

Certain embodiments include systems and methods providing use of MonteCarlo simulation integrated with PERT and CPM in healthcare relatedprocesses to simulate “what if” scenarios, calculate schedule risk, findfeasible process solutions, replay events of historical record in anemulation and as a transfer function that can be called from anoptimization.

Certain embodiments include systems and methods providing use ofsingularly and/or in combination of discrete event, agent based,continuous or system dynamics and Monte Carlo simulation integrated withPERT and CPM in inter-related healthcare delivery or support processesin and amongst departments or hospital networks in a region to simulate“what if” scenarios, calculate schedule risk, find feasible processsolutions, replay events of historical record in an emulation and as atransfer function that can be called from an optimization.

Certain embodiments provide systems and methods to replay “what was”emulations of actual processes at one or a plurality of hospitals forindividual or team learning with an interactive user interface orstructured command.

Certain embodiments provide systems and methods utilizing “what was”emulation or simulation for activity based costing estimates, checks,validations, re-engineering or justification. Certain embodimentsprovide systems and methods utilizing “what was” emulation or simulationfor billing. Certain embodiments provide systems and methods to replay“what was” for team, peer and operational learning. Certain embodimentsprovide systems and methods utilizing “what was” historical records totrain decision support algorithms that include case-based reasoning,rule-based, fuzzy logic, example-based evidentiary reasoning, neuralnet, regressive, heuristic or other artificial intelligence basedalgorithms.

Certain embodiments provide systems and methods utilizing “what was” tocalibrate simulation model(s). Certain embodiments provide systems andmethods utilizing “what was” for process study, analysis, peer review,re-engineering, best practice sharing and benchmarking.

Certain embodiments provide systems and methods utilizing the plannedschedule and comparing it live against “what-is” for process study,analysis, peer review, re-engineering, best practice sharing, decisionsupport and benchmarking.

Certain embodiments provide systems and methods utilizing “what was” forcase cart and equipment, assets and resources preferences and decisionsupport process study, analysis, peer review, re-engineering, bestpractice sharing, decision support and benchmarking.

Certain embodiments provide systems and methods to provide decisionsupport as to where best, given one or more process objectives, to addand insert task(s).

Certain embodiments provide systems and methods to provide decisionsupport as to what tasks to drop and resources to release from specifictasks in order to best meet objective(s) of the process when theschedule changes fir any reason.

Certain embodiments provide systems and methods to adjust blocks ofdynamically allocated time.

Certain embodiments provide systems and methods to dynamically adjustupper and lower specification limits for various alerts.

Certain embodiments provide systems and methods to call clinicalprotocol into the CPM structure for schedule monitoring.

Certain embodiments provide a capability to create an exception reportfor schedule and protocol mismatch.

Certain embodiments provide an ability to configure protocol andschedule mismatch presentation where they are matched,schedules>protocol, protocol>schedule as an exception.

Certain embodiments provide a capability to integrate equipment,biometrical or personal information into a schedule in order todetermine impact on the schedule.

Certain embodiments provide an ability to use optical and RFID an EMRdynamic data to assess/forecast the status or degree of taskcompleteness and resources utilization.

Certain embodiments provide an ability to incorporate equipmentprognostics into the schedule risk calculation(s).

Certain embodiments provide an ability to incorporate PDF frequencyhistograms, look-up tables & cumulative probability distributions forprocedure duration, staff availability and skill, equipment reliabilityinto the CPM and simulation method assumptions.

Certain embodiments provide an ability to simulate forward to explorefeasible and more robust or flexible alternative schedules that satisfyone or more objectives.

Certain embodiments provide coupling of linear programming and/orstochastic optimization to trade off process objectives for one or moreprocess objectives.

Certain embodiments provide systems and methods to simulate forward toexplore alternative schedules using CPM and PERT task and assetrelationship methods where rule based or simulations utilizing agentbased, discrete event, Monte Carlo or continuous differential methodsare used to forecast assets of the process behavior.

Certain embodiments provide an ability to forecast the long termrelationships between investment in resources, staff, information,healthcare providers with the change in capability and the attraction ofmore patients with resulting revenues.

Certain embodiments provide an ability to use ‘what ifs’ to planequipment and surgical inventory logistics.

Certain embodiments provide an ability to use analytical workflowcoordinating the analytical task in an automated and semi-automatedfashion.

Certain embodiments provide an ability to summarize the state of theprocess in either actual time, simulated forward or the emulatedhistorical record and provide a visual indicator such asred/yellow/green, shapes, happy faces, or other audible and visualrepresentation.

Certain embodiments provide an ability to use probabilistic setpoints tosummarize the state of the schedule performance or risk in either actualtime, simulated forward or the emulated historical record.

Several embodiments are described above with reference to drawings.These drawings illustrate certain details of specific embodiments thatimplement the systems and methods and programs of the present invention.However, describing the invention with drawings should not be construedas imposing on the invention any limitations associated with featuresshown in the drawings. The present invention contemplates methods,systems and program products on any machine-readable media foraccomplishing its operations. As noted above, the embodiments of thepresent invention may be implemented using an existing computerprocessor, or by a special purpose computer processor incorporated forthis or another purpose or by a hardwired system.

As noted above, embodiments within the scope of the present inventioninclude program products comprising machine-readable media for carryingor having machine-executable instructions or data structures storedthereon. Such machine-readable media can be any available media that canbe accessed by a general purpose or special purpose computer or othermachine with a processor. By way of example, such machine-readable mediamay comprise RAM, ROM, PROM, EPROM, EEPROM, Flash, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to carry or store desiredprogram code in the form of machine-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer or other machine with a processor. When information istransferred or provided over a network or another communicationsconnection (either hardwired, wireless, or a combination of hardwired orwireless) to a machine, the machine properly views the connection as amachine-readable medium. Thus, any such a connection is properly termeda machine-readable medium. Combinations of the above are also includedwithin the scope of machine-readable media. Machine-executableinstructions comprise, for example, instructions and data which cause ageneral purpose computer, special purpose computer, or special purposeprocessing machines to perform a certain function or group of functions.

Embodiments of the invention are described in the general context ofmethod steps which may be implemented in one embodiment by a programproduct including machine-executable instructions, such as program code,for example in the form of program modules executed by machines innetworked environments. Generally, program modules include routines,programs, objects, components, data structures, etc., that performparticular tasks or implement particular abstract data types.Machine-executable instructions, associated data structures, and programmodules represent examples of program code for executing steps of themethods disclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Embodiments of the present invention may be practiced in a networkedenvironment using logical connections to one or more remote computershaving processors. Logical connections may include a local area network(LAN) and a wide area network (WAN) that are presented here by way ofexample and not limitation. Such networking environments are commonplacein office-wide or enterprise-wide computer networks, intranets and theInternet and may use a wide variety of different communicationprotocols. Those skilled in the art will appreciate that such networkcomputing environments will typically encompass many types of computersystem configurations, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. Embodiments of the invention may also be practiced in distributedcomputing environments where tasks are performed by local and remoteprocessing devices that are linked (either by hardwired links, wirelesslinks, or by a combination of hardwired or wireless links) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

An exemplary system for implementing the overall system or portions ofthe invention might include a general purpose computing device in theform of a computer, including a processing unit, a system memory, and asystem bus that couples various system components including the systemmemory to the processing unit. The system memory may include read onlymemory (ROM) and random access memory (RAM). The computer may alsoinclude a magnetic hard disk drive for reading from and writing to amagnetic hard disk, a magnetic disk drive for reading from or writing toa removable magnetic disk, and an optical disk drive for reading from orwriting to a removable optical disk such as a CD ROM or other opticalmedia. The drives and their associated machine-readable media providenonvolatile storage of machine-executable instructions, data structures,program modules and other data for the computer.

The foregoing description of embodiments of the invention has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdisclosed, and modifications and variations are possible in light of theabove teachings or may be acquired from practice of the invention. Theembodiments were chosen and described in order to explain the principalsof the invention and its practical application to enable one skilled inthe art to utilize the invention in various embodiments and with variousmodifications as are suited to the particular use contemplated.

Those skilled in the art will appreciate that the embodiments disclosedherein may be applied to the formation of any clinicalscheduling/planning system. Certain features of the embodiments of theclaimed subject matter have been illustrated as described herein;however, many modifications, substitutions, changes and equivalents willnow occur to those skilled in the art. Additionally, while severalfunctional blocks and relations between them have been described indetail, it is contemplated by those of skill in the art that several ofthe operations may be performed without the use of the others, oradditional functions or relationships between functions may beestablished and still be in accordance with the claimed subject matter.It is, therefore, to be understood that the appended claims are intendedto cover all such modifications and changes as fall within the truespirit of the embodiments of the claimed subject matter.

1. An apparatus comprising: memory including instructions; communicationcircuitry to communicate with one or more healthcare systems; and atleast one processor to execute the instructions to at least: load aschedule of tasks and associated asset availability from the one or morehealthcare systems; process, using at least one of a simulation or anartificial intelligence model, the schedule of tasks using theassociated asset availability and a distribution of probabilitiesassociated with durations of the tasks in the schedule of tasks;generate a user interface including views from the simulation of: a) theschedule of tasks, b) a difference between the schedule of tasks andcurrent process operations, and c) a probabilistic prediction of afuture schedule; and output, in response to a selection of at least oneof a), b), or c), instructions to the one or more healthcare systems foran updated schedule of tasks based on the selected at least one of a),b), or c).
 2. The apparatus of claim 1, wherein the user interface is tofacilitate monitoring of activity and tracking the schedule of tasks fora time period.
 3. The apparatus of claim 1, wherein the user interfaceis to visualize a variation of schedule task times, visualize ascheduling opportunity and associated constraint, and view schedule riskinformation with respect to the schedule of tasks.
 4. The apparatus ofclaim 1, wherein the artificial intelligence model includes at least oneof a case-based reasoning model, a rule-based model, a fuzzy logicmodel, an example-based evidentiary reasoning model, an artificialneural network model, a regressive model, or a heuristic model.
 5. Theapparatus of claim 1, wherein the user interface is to display thedistribution of probabilities and a cumulative probability for a blockof time over time.
 6. The apparatus of claim 1, wherein the userinterface is to display an analytical roadmap of tasks, assets, andprocess interdependencies.
 7. The apparatus of claim 6, wherein the userinterface is to display a measure of availability, calculation of acritical path, and use of exogenous variation, probabilistic duration,availability and reliability to calculate at least one of a) aprobability of events beginning and ending at scheduled times, or b) astart time and a completion time to achieve a probability estimate. 8.The apparatus of claim 7, wherein the user interface is to display, asdetermined by the at least one processor, asset interdependencies and adependency state design associated with the critical path calculation.9. The apparatus of claim 8, wherein the user interface is to display,as determined by the at least one processor, a base schedule, the baseschedule determined based on a variation in asset reliability, staffattendance, and a probability duration estimate.
 10. The apparatus ofclaim 9, wherein the at least one processor is to simulate the baseschedule with a schedule risk, the at least one processor to applydecision support with respect to the base schedule based on the schedulerisk.
 11. At least one non-transitory computer-readable storage mediumcomprising instructions that, when executed, cause at least oneprocessor to at least: load a schedule of tasks and associated assetavailability from one or more healthcare systems; process, using atleast one of a simulation or an artificial intelligence model, theschedule of tasks using the associated asset availability and adistribution of probabilities associated with durations of the tasks inthe schedule of tasks; generate a user interface including views fromthe simulation of: a) the schedule of tasks, b) a difference between theschedule of tasks and current process operations, and c) a probabilisticprediction of a future schedule; and output, in response to a selectionof at least one of a), b), or c), instructions to the one or morehealthcare systems for an updated schedule of tasks based on theselected at least one of a), b), or c).
 12. The at least onenon-transitory computer-readable storage medium of claim 11, wherein theinstructions, when executed, cause the user interface to monitoractivity and track the schedule of tasks for a time period.
 13. The atleast one non-transitory computer-readable storage medium of claim 11,wherein the instructions, when executed, cause the user interface tovisualize a variation of schedule task times, visualize a schedulingopportunity and associated constraint, and view schedule riskinformation with respect to the schedule of tasks.
 14. The at least onenon-transitory computer-readable storage medium of claim 11, wherein theartificial intelligence model includes at least one of a case-basedreasoning model, a rule-based model, a fuzzy logic model, anexample-based evidentiary reasoning model, an artificial neural networkmodel, a regressive model, or a heuristic model.
 15. The at least onenon-transitory computer-readable storage medium of claim 11, wherein theinstructions, when executed, cause the user interface to display thedistribution of probabilities and a cumulative probability for a blockof time over time.
 16. The at least one non-transitory computer-readablestorage medium of claim 11, wherein the instructions, when executed,cause the user interface to display an analytical roadmap of tasks,assets, and process interdependencies.
 17. The at least onenon-transitory computer-readable storage medium of claim 16, wherein theinstructions, when executed, cause the user interface to display ameasure of availability, calculation of a critical path, and use ofexogenous variation, probabilistic duration, availability andreliability to calculate at least one of a) a probability of eventsbeginning and ending at scheduled times, or b) a start time and acompletion time to achieve a probability estimate.
 18. The at least onenon-transitory computer-readable storage medium of claim 17, wherein theinstructions, when executed, cause the user interface to display, asdetermined by the at least one processor, asset interdependencies and adependency state design associated with the critical path calculation.19. The at least one non-transitory computer-readable storage medium ofclaim 18, wherein the instructions, when executed, cause the userinterface to display, as determined by the at least one processor, abase schedule, the base schedule determined based on a variation inasset reliability, staff attendance, and a probability durationestimate.
 20. The at least one non-transitory computer-readable storagemedium of claim 19, wherein the instructions, when executed, cause theat least one processor to: simulate the base schedule with a schedulerisk; and apply decision support with respect to the base schedule basedon the schedule risk.